With the arrival of multimedia information age, various multimedia processing and communication technologies, especially video processing and video communication technologies emerge. Accordingly, quality evaluation for digital video is becoming more and more important. The digital video is the most primary media form for multimedia video communication nowadays. A network streaming media (e.g. a network movie, a network TV and so on), a video conference and a video telephone etc. are all multimedia applications based on video. Customer satisfaction on a multimedia application service relies on video quality to a great extent. Digital video signal transmission includes some processes such as sampling, quantifying, compression coding, network transmitting, decoding and restoring for an analog video signal, wherein in some processes, especially, network transmitting without guaranteeing QoS (Quality of Service) (e.g. packet transmission network), errors and information distortion may be introduced in each process, thus leading to a decreased customer satisfaction. Multimedia video communication quality is a measurement for measuring distortion of a digital multimedia signal relative to an original signal. Video quality evaluation plays a very important role in the field of video compression, processing and video communication. Performance of a real-time or non real-time video system and the QoS of various video communication transmission channels are finally reflected by the video quality, and feedback for adjusting parameters of a codec or a channel is given, thus guaranteeing the video quality within an acceptable range. An easy to understand measurement for output video quality of various different codecs is presented so that the performance of the codec may be designed, evaluated and optimized. A graphics and image display system according with a human visual model is designed and optimized.
Two factors affecting the video quality in a wireless and IP (Internet Protocol) transmission video system are: one is that the video quality is degraded due to video compression; the other is that the video quality is affected by channel packet loss and random error codes due to relatively bad channel environment, especially video contents are damaged by various errors of header information and motion vectors. Some unendurable mosaic blocks are often formed in these damaged video contents, and greatly affect the subjective quality of the recovered video. Therefore, video quality measurement, feedback correction and test development based on the measurement are desired for the current multimedia video communication evolving towards a direction of wireless and IP transmission.
The video quality evaluation is also very significant for video communication equipment manufacturers and telecom operators. For the equipment manufacturers, providing a convictive video quality evaluation result of a system may greatly facilitate the sale of their products. For the operators, the evaluation data of the video quality may be used for popularization and propagation of their services. In addition, developing an automatic real-time video quality evaluation method, based on which both the manufacturers and the operators are capable of performing real-time monitoring to video equipment, will facilitate trouble shooting and fault diagnosis.
The video quality evaluation may be classified into subjective quality evaluation and objective quality evaluation. A result of the subjective quality evaluation is reliable, but the evaluation for video subjective quality is strict with a Human Test Subject because it relies on participation of the Human Test Subject, the process of which is complicated and hard to be widely applied, especially for evaluation in an application environment with real-time demand. The objective quality evaluation measures the video quality using a quantifying method from another aspect, and may be performed automatically with high efficiency and without human participation.
The objective quality evaluation may be further classified into three categories:
(1) Full Reference Model in need of a full original video sequence;
(2) Partial Reference Model only in need of partial statistic characteristics of an original video sequence;
(3) Referenceless Model without need of any information of an original video sequence.
The full reference and the partial reference evaluation methods are not widely used because the needed reference video sequence generally cannot be obtained in practical applications, so a new objective real-time video quality evaluation method without need of any reference information is desired. At present, the VQEG (Video Quality Experts Group) is dedicated to research methods and constitute standards for the referenceless video quality evaluation.
PSNR (Peak Signal-to-Noise Ratio) is most widely used among numerous objective quality evaluation indications, because it is easy to be calculated, has apparent physical meaning and may actually reflect a distortion degree of an image.
Digital media is easy to be accessed, copied, transmitted and edited, while some problems such as pirating a digital media copyright and interpolating digital media contents will arise. Thus a digital watermark technique early used for protecting a digital media copyright is presented.
The digital watermark technique is developed and widely used in recent years. Watermark information embedded in original media data always coexists with the original media data by embedding a series of information in the original media data, thus the copyright of the original media data and the integrity of contents may be protected. With development of technologies, besides copyright protection, the digital watermark technique may be used in many other fields. For example, the multimedia video communication quality measurement mentioned above may be achieved by embedding and extracting a digital watermark, which is a Partial Reference Model method, without knowing an original video image but indirectly reflecting status of the original image with reference to a watermark image before channel transmission and a distorted watermark image after channel transmission, accordingly the quality measurement, calculation and evaluation are performed.
The digital watermark may be embedded in any portion of the original data, but its effect on the original data needs to be reduced to the utmost extent. The digital watermark technology may be classified into a digital watermark technology of the spatial domain and a digital watermark technology of the transform domain according to digital watermark embedding approaches. In the digital watermark technology, watermark information is directly embedded in the spatial domain of a media. For example, the information is embedded in pixels of an image. In the digital watermark technology of the transform domain, a transform is performed on a media firstly, such as DFT (Discrete Fourier Transform), DCT (Discrete Cosine Transform) or DWT (Discrete Wavelet Transform) etc., and then the watermark information is embedded in the transform domain.
FIG. 1 shows a block diagram illustrating a principle of a digital watermark. In this figure, the master media I0 generally is original or compressed multimedia data such as video or audio etc., and the data b0 to be hidden only has less data in comparison with I0. The difference between the media I1 embedded with a watermark and I0 is distortion caused by the embedded watermark. In general, such distortion is not desired to be perceived by human. The media I2 is obtained by performing some processing on I1, such as data compression, noise contamination and intended attacks to the watermark, which may be regarded as noise. Therefore, the watermark b1 extracted from I2 may be distorted to a certain extent in comparison with the original watermark b0. If I2 is identical with I1, the watermark b1 extracted from I2 should be identical with the original watermark b0.
A common mathematical model for watermark embedding and extracting is: I0 and I1 respectively represent the original data and the data embedded with the watermark, b0 represents the original watermark, the watermark embedding process may be expressed as: I1=I0+f(I0,b0), wherein f(I0,b0) represents a watermark embedding algorithm. The watermark measurement process may be expressed as: if H0: b1=I2−I0=N, the watermark does not exist; if H1: b1=I2−I0=b0+N, the watermark exists. N represents noise. For example, the noise may be caused by data compression, noise contamination and intended attacks to the watermark etc. The data embedded with the watermark will be distorted to a certain extent after processing, so the watermark measured from the processed data may be different from the original watermark to a certain extent.
Watermark measurement technology is commonly implemented by the classical Signal Detection technology, which is used for researching how to determine whether a destination signal exists in noise, such as whether a reflecting signal from a destination is contained in a radar echo signal, and if the destination signal exists, how to perform optimal signal extraction using a statistic principle. Statistic Hypothesis Test/Validation is used for determining whether a signal exists in noise. A watermark measurement process includes: presenting two hypotheses H0 and H1, determining which hypothesis is true according to a test result, and accordingly knowing whether a watermark exists.
At present, all the methods for multimedia (video and images) quality evaluation based on digital watermark comply with a same principle. FIG. 2 shows a block diagram illustrating a principle of a video communication quality measurement based on a digital watermark, and the basic principle of which will be described hereinafter.
A watermark image, which is very small relative to an original image, is embedded in the original image, thus the added additional data quantity is negligible in comparison with the data quantity of the original image, and has little effect on the multimedia quality. The original image embedded with the watermark is distorted after passing through a transmission channel, and the watermark image extracted from which will be accordingly distorted. The watermark is embedded in the original image everywhere, so the quality measurement for the watermark image is equivalent to the measurement for the original image after distributed sampling. As long as the distribution of the watermark is uniform enough, the quality of the distorted image may be sufficiently reflected by measuring the quality of the watermark image. While the original image is shared by both sides of a communication, the receiving side measures communication quality by comparing the recovered watermark image with the known original watermark image, thus the quality evaluation for the watermark is actually a kind of evaluation with reference.
FIG. 3 shows an example of measuring the multimedia video communication quality based on a digital watermark. The left is an original watermark image, which is a black-and-white (easy to determine error positions) bi-level image. The middle is a distorted image of an international standard test image Lena after being embedded with the watermark and JPEG (Joint Photographic Experts Group) standard compression and network packet loss during a transmission process, wherein there are three obvious block distortions in the image. The right is a recovered watermark extracted from the distorted Lena image, wherein scatteredly distributed black spots and white spots are distortions due to the compression, while big black and white blocks, the positions of which correspond to three obvious distortions in the recovered Lena image, are caused by errors such as packet loss.